OpenRaiser/Intern-Atlas huggingface dataset:
Cross-Spectral Iris Matching Using Conditional Coupled GAN
Venue: arXiv cs.CV 2020
Year: 2020

Cross-spectral iris recognition is emerging as a promising biometric approach to authenticating the identity of individuals. However, matching iris images acquired at different spectral bands shows significant performance degradation when compared to single-band near-infrared (NIR) matching due to the spectral gap between iris images obtained in the NIR and visual-light (VIS) spectra. Although researchers have recently focused on deep-learning-based approaches to recover invariant representative features for more accurate recognition performance, the existing methods cannot achieve the expected accuracy required for commercial applications. Hence, in this paper, we propose a conditional coupled generative adversarial network (CpGAN) architecture for cross-spectral iris recognition by projecting the VIS and NIR iris images into a low-dimensional embedding domain to explore the hidden relationship between them. The conditional CpGAN framework consists of a pair of GAN-based networks, one responsible for retrieving images in the visible domain and other responsible for retrieving images in the NIR domain. Both networks try to map the data into a common embedding subspace to ensure maximum pair-wise similarity between the feature vectors from the two iris modalities of the same subject. To prove the usefulness of our proposed approach, extensive experimental results obtained on the PolyU dataset are compared to existing state-of-the-art cross-spectral recognition methods.
Separation Results for Boolean Function Classes
Venue: arXiv cs.CC 2020
Year: 2020

We show (almost) separation between certain important classes of Boolean functions. The technique that we use is to show that the total influence of functions in one class is less than the total influence of functions in the other class. In particular, we show (almost) separation of several classes of Boolean functions which have been studied in the coding theory and cryptography from classes which have been studied in combinatorics and complexity theory.
FasterRCNN Monitoring of Road Damages: Competition and Deployment
Venue: arXiv cs.CV 2020
Year: 2020

Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world. An important prerequisite for efficient infrastructure maintenance is to continuously monitor (i.e., quantify the level of safety and reliability) the state of very large structures. Meanwhile, computer vision has made impressive strides in recent years, mainly due to successful applications of deep learning models. These novel progresses are allowing the automation of vision tasks, which were previously impossible to automate, offering promising possibilities to assist administrators in optimizing their infrastructure maintenance operations. In this context, the IEEE 2020 global Road Damage Detection (RDD) Challenge is giving an opportunity for deep learning and computer vision researchers to get involved and help accurately track pavement damages on road networks. This paper proposes two contributions to that topic: In a first part, we detail our solution to the RDD Challenge. In a second part, we present our efforts in deploying our model on a local road network, explaining the proposed methodology and encountered challenges.
Spikyball sampling: Exploring large networks via an inhomogeneous filtered diffusion
Venue: arXiv cs.SI 2020
Year: 2020

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatically sample user-defined relevant parts of a network. Starting from a few selected places in the network and a reduced set of expansion rules, the method adopts a filtered breadth-first search approach, that expands through edges and nodes matching these properties. Moreover, the expansion is performed over a random subset of neighbors at each step to mitigate further the overwhelming number of connections that may exist in large graphs. This carries the image of a "spiky" expansion. We show that this approach generalize previous exploration sampling methods, such as Snowball or Forest Fire and extend them. We demonstrate its ability to capture groups of nodes with high interactions while discarding weakly connected nodes that are often numerous in social networks and may hide important structures.
Automated Metadata Harmonization Using Entity Resolution & Contextual Embedding
Venue: arXiv cs.DB 2020
Year: 2020

ML Data Curation process typically consist of heterogeneous & federated source systems with varied schema structures; requiring curation process to standardize metadata from different schemas to an inter-operable schema. This manual process of Metadata Harmonization & cataloging slows efficiency of ML-Ops lifecycle. We demonstrate automation of this step with the help of entity resolution methods & also by using Cogntive Database's Db2Vec embedding approach to capture hidden inter-column & intra-column relationships which detect similarity of metadata and then predict metadata columns from source schemas to any standardized schemas. Apart from matching schemas, we demonstrate that it can also infer the correct ontological structure of the target data model.
ParaLarH: Parallel FPGA Router based upon Lagrange Heuristics
Venue: arXiv cs.DC 2020
Year: 2020

Routing of the nets in Field Programmable Gate Array (FPGA) design flow is one of the most time consuming steps. Although Versatile Place and Route (VPR), which is a commonly used algorithm for this purpose, routes effectively, it is slow in execution. One way to accelerate this design flow is to use parallelization. Since VPR is intrinsically sequential, a set of parallel algorithms have been recently proposed for this purpose (ParaLaR and ParaLarPD). These algorithms formulate the routing process as a Linear Program (LP) and solve it using the Lagrange relaxation, the sub-gradient method, and the Steiner tree algorithm. Out of the many metrics available to check the effectiveness of routing, ParaLarPD, which is an improved version of ParaLaR, suffers from large violations in the constraints of the LP problem (which is related to the minimum channel width metric) as well as an easily measurable critical path delay metric that can be improved further. In this paper, we introduce a set of novel Lagrange heuristics that improve the Lagrange relaxation process. When tested on the MCNC benchmark circuits, on an average, this leads to halving of the constraints violation, up to 10% improvement in the minimum channel width, and up to 8% reduction in the critical path delay as obtained from ParaLarPD. We term our new algorithm as ParaLarH. Due to the increased work in the Lagrange relaxation process, as compared to ParaLarPD, ParaLarH does slightly deteriorate the speedup obtained because of parallelization, however, this aspect is easily compensated by using more number of threads.
What are the Statistical Limits of Offline RL with Linear Function Approximation?
Venue: arXiv cs.LG 2020
Year: 2020

Offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of (causal) sequential decision making strategies. The hope is that offline reinforcement learning coupled with function approximation methods (to deal with the curse of dimensionality) can provide a means to help alleviate the excessive sample complexity burden in modern sequential decision making problems. However, the extent to which this broader approach can be effective is not well understood, where the literature largely consists of sufficient conditions. This work focuses on the basic question of what are necessary representational and distributional conditions that permit provable sample-efficient offline reinforcement learning. Perhaps surprisingly, our main result shows that even if: i) we have realizability in that the true value function of \emph{every} policy is linear in a given set of features and 2) our off-policy data has good coverage over all features (under a strong spectral condition), then any algorithm still (information-theoretically) requires a number of offline samples that is exponential in the problem horizon in order to non-trivially estimate the value of \emph{any} given policy. Our results highlight that sample-efficient offline policy evaluation is simply not possible unless significantly stronger conditions hold; such conditions include either having low distribution shift (where the offline data distribution is close to the distribution of the policy to be evaluated) or significantly stronger representational conditions (beyond realizability).
Neural-Symbolic Integration: A Compositional Perspective
Venue: arXiv cs.AI 2020
Year: 2020

Despite significant progress in the development of neural-symbolic frameworks, the question of how to integrate a neural and a symbolic system in a \emph{compositional} manner remains open. Our work seeks to fill this gap by treating these two systems as black boxes to be integrated as modules into a single architecture, without making assumptions on their internal structure and semantics. Instead, we expect only that each module exposes certain methods for accessing the functions that the module implements: the symbolic module exposes a deduction method for computing the function's output on a given input, and an abduction method for computing the function's inputs for a given output; the neural module exposes a deduction method for computing the function's output on a given input, and an induction method for updating the function given input-output training instances. We are, then, able to show that a symbolic module -- with any choice for syntax and semantics, as long as the deduction and abduction methods are exposed -- can be cleanly integrated with a neural module, and facilitate the latter's efficient training, achieving empirical performance that exceeds that of previous work.
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
Venue: arXiv cs.RO 2020
Year: 2020

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment. To combine the benefits of both approaches, we propose motion planner augmented RL (MoPA-RL) which augments the action space of an RL agent with the long-horizon planning capabilities of motion planners. Based on the magnitude of the action, our approach smoothly transitions between directly executing the action and invoking a motion planner. We evaluate our approach on various simulated manipulation tasks and compare it to alternative action spaces in terms of learning efficiency and safety. The experiments demonstrate that MoPA-RL increases learning efficiency, leads to a faster exploration, and results in safer policies that avoid collisions with the environment. Videos and code are available at https://clvrai.com/mopa-rl .
Prediction of Temperature and Rainfall in Bangladesh using Long Short Term Memory Recurrent Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Temperature and rainfall have a significant impact on economic growth as well as the outbreak of seasonal diseases in a region. In spite of that inadequate studies have been carried out for analyzing the weather pattern of Bangladesh implementing the artificial neural network. Therefore, in this study, we are implementing a Long Short-term Memory (LSTM) model to forecast the month-wise temperature and rainfall by analyzing 115 years (1901-2015) of weather data of Bangladesh. The LSTM model has shown a mean error of -0.38oC in case of predicting the month-wise temperature for 2 years and -17.64mm in case of predicting the rainfall. This prediction model can help to understand the weather pattern changes as well as studying seasonal diseases of Bangladesh whose outbreaks are dependent on regional temperature and/or rainfall.
Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset
Venue: arXiv cs.HC 2020
Year: 2020

Modern social intelligence includes the ability to watch videos and answer questions about social and theory-of-mind-related content, e.g., for a scene in Harry Potter, "Is the father really upset about the boys flying the car?" Social visual question answering (social VQA) is emerging as a valuable methodology for studying social reasoning in both humans (e.g., children with autism) and AI agents. However, this problem space spans enormous variations in both videos and questions. We discuss methods for creating and characterizing social VQA datasets, including 1) crowdsourcing versus in-house authoring, including sample comparisons of two new datasets that we created (TinySocial-Crowd and TinySocial-InHouse) and the previously existing Social-IQ dataset; 2) a new rubric for characterizing the difficulty and content of a given video; and 3) a new rubric for characterizing question types. We close by describing how having well-characterized social VQA datasets will enhance the explainability of AI agents and can also inform assessments and educational interventions for people.
A New Charter of Ethics and Rights of Artificial Consciousness in a Human World
Venue: arXiv cs.CY 2020
Year: 2020

Taking the stance that artificially conscious agents should be given human-like rights, in this paper we attempt to define consciousness, aggregate existing universal human rights, analyze robotic laws with roots in both reality and science fiction, and synthesize everything to create a new robot-ethical charter. By restricting the problem-space of possible levels of conscious beings to human-like, we succeed in developing a working definition of consciousness for social strong AI which focuses on human-like creativity being exhibited as a third-person observable phenomenon. Creativity is then extrapolated to represent first-person functionality, fulfilling the first/third-person feature of consciousness. Next, several sources of existing rights and rules, both for humans and robots, are analyzed and, along with supplementary informal reports, synthesized to create articles for an additive charter which compliments the United Nation's Universal Declaration of Human Rights. Finally, the charter is presented and the paper concludes with the conditions for amending the charter, as well as recommendations for further charters.
Saudi Arabian Parents' Perception of Online Marital Matchmaking Technologies
Venue: arXiv cs.CY 2020
Year: 2020

Finding a date or a spouse online is usually considered an individualistic endeavor in Western cultures. This presents a challenge for collectivist non-Western cultures such as Saudi Arabia where choosing a spouse is viewed as a union of two families with parents of both spouses being heavily involved. Our work aims to investigate how Saudi Arabian parents view the utilization of technology by their young adults to seek potential spouses online. We report our findings of interviews conducted with 16 Saudi Arabian parents (8 fathers, 6 mothers and 1 couple). We generate qualitative themes that provide insights about how parents wanted to preserve their values, integrate technology into the traditional process and protect their young adults from potential harms. These themes lead to implications for designing suitable marital matchmaking technologies in Saudi Arabia and opportunities for future work.
Training Opportunities for Intelligent Transport Systems and Cooperative Intelligent Transport Systems
Venue: arXiv cs.CY 2020
Year: 2020

Intelligent Transport Systems (ITS) and Cooperative Intelligent Transport Systems (C-ITS) are of high significance, mainly due to the benefits they have in terms of operation of the transport network. Despite ITS and C-ITS importance in the operation of the transport network, there is a major knowledge gap regarding their development, way of operation and significance worldwide and especially among the responsible for their deployment public authorities. In order for such fragmentations to be tackled, an online training platform concerning the operation and impacts of ITS and C-ITS has been designed in the framework of the European Union Horizon 2020 funded CAPITAL project. In order for the proper design of the CAPITAL Online Training Platform, two main approaches have been studied: capacity building and massive open online courses. The present study provides insight regarding the design and the context of the CAPITAL Online Training Platform.
Fading memory echo state networks are universal
Venue: arXiv cs.NE 2020
Year: 2020

Echo state networks (ESNs) have been recently proved to be universal approximants for input/output systems with respect to various $L ^p$-type criteria. When $1\leq p< \infty$, only $p$-integrability hypotheses need to be imposed, while in the case $p=\infty$ a uniform boundedness hypotheses on the inputs is required. This note shows that, in the last case, a universal family of ESNs can be constructed that contains exclusively elements that have the echo state and the fading memory properties. This conclusion could not be drawn with the results and methods available so far in the literature.
Dynamics and Domain Randomized Gait Modulation with Bezier Curves for Sim-to-Real Legged Locomotion
Venue: arXiv cs.RO 2020
Year: 2020

We present a sim-to-real framework that uses dynamics and domain randomized offline reinforcement learning to enhance open-loop gaits for legged robots, allowing them to traverse uneven terrain without sensing foot impacts. Our approach, D$^2$-Randomized Gait Modulation with Bezier Curves (D$^2$-GMBC), uses augmented random search with randomized dynamics and terrain to train, in simulation, a policy that modifies the parameters and output of an open-loop Bezier curve gait generator for quadrupedal robots. The policy, using only inertial measurements, enables the robot to traverse unknown rough terrain, even when the robot's physical parameters do not match the open-loop model. We compare the resulting policy to hand-tuned Bezier Curve gaits and to policies trained without randomization, both in simulation and on a real quadrupedal robot. With D$^2$-GMBC, across a variety of experiments on unobserved and unknown uneven terrain, the robot walks significantly farther than with either hand-tuned gaits or gaits learned without domain randomization. Additionally, using D$^2$-GMBC, the robot can walk laterally and rotate while on the rough terrain, even though it was trained only for forward walking.
A Multilinear Sampling Algorithm to Estimate Shapley Values
Venue: arXiv cs.LG 2020
Year: 2020

Shapley values are great analytical tools in game theory to measure the importance of a player in a game. Due to their axiomatic and desirable properties such as efficiency, they have become popular for feature importance analysis in data science and machine learning. However, the time complexity to compute Shapley values based on the original formula is exponential, and as the number of features increases, this becomes infeasible. Castro et al. [1] developed a sampling algorithm, to estimate Shapley values. In this work, we propose a new sampling method based on a multilinear extension technique as applied in game theory. The aim is to provide a more efficient (sampling) method for estimating Shapley values. Our method is applicable to any machine learning model, in particular for either multi-class classifications or regression problems. We apply the method to estimate Shapley values for multilayer perceptrons (MLPs) and through experimentation on two datasets, we demonstrate that our method provides more accurate estimations of the Shapley values by reducing the variance of the sampling statistics.
Few-shot Image Recognition with Manifolds
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we extend the traditional few-shot learning (FSL) problem to the situation when the source-domain data is not accessible but only high-level information in the form of class prototypes is available. This limited information setup for the FSL problem deserves much attention due to its implication of privacy-preserving inaccessibility to the source-domain data but it has rarely been addressed before. Because of limited training data, we propose a non-parametric approach to this FSL problem by assuming that all the class prototypes are structurally arranged on a manifold. Accordingly, we estimate the novel-class prototype locations by projecting the few-shot samples onto the average of the subspaces on which the surrounding classes lie. During classification, we again exploit the structural arrangement of the categories by inducing a Markov chain on the graph constructed with the class prototypes. This manifold distance obtained using the Markov chain is expected to produce better results compared to a traditional nearest-neighbor-based Euclidean distance. To evaluate our proposed framework, we have tested it on two image datasets - the large-scale ImageNet and the small-scale but fine-grained CUB-200. We have also studied parameter sensitivity to better understand our framework.
The Pursuit of Algorithmic Fairness: On "Correcting" Algorithmic Unfairness in a Child Welfare Reunification Success Classifier
Venue: arXiv cs.LG 2020
Year: 2020

The algorithmic fairness of predictive analytic tools in the public sector has increasingly become a topic of rigorous exploration. While instruments pertaining to criminal recidivism and academic admissions, for example, have garnered much attention, the predictive instruments of Child Welfare jurisdictions have received considerably less attention. This is in part because comparatively few such instruments exist and because even fewer have been scrutinized through the lens of algorithmic fairness. In this work, we seek to address both of these gaps. To this end, a novel classification algorithm for predicting reunification success within Oregon Child Welfare is presented, including all of the relevant details associated with building such an instrument. The purpose of this tool is to maximize the number of stable reunifications and identify potentially unstable reunifications which may require additional resources and scrutiny. Additionally, because the algorithmic fairness of the resulting tool, if left unaltered, is unquestionably lacking, the utilized procedure for mitigating such unfairness is presented, along with the rationale behind each difficult and unavoidable choice. This procedure, though similar to other post-processing group-specific thresholding methods, is novel in its use of a penalized optimizer and contextually requisite subsampling. These novel methodological components yield a rich and informative empirical understanding of the trade-off continuum between fairness and accuracy. As the developed procedure is generalizable across a variety of group-level definitions of algorithmic fairness, as well as across an arbitrary number of protected attribute levels and risk thresholds, the approach is broadly applicable both within and beyond Child Welfare.